Fluctuation Effects on the Transport Properties of Unitary Fermi Gases

In this letter, we investigate the fluctuation effects on the transport properties of unitary Fermi gases in the vicinity of the superfluid transition temperature $T_c$. Based on the time-dependent Ginzburg-Landau formalism of the BEC-BCS crossover, we investigate both the residual resistivity below $T_c$ induced by phase slips and the paraconductivity above $T_c$ due to pair fluctuations. These two effects have been well studied in the weak coupling BCS superconductor, and here we generalize them to the unitary regime of ultracold Fermi gases. We find that while the residual resistivity below $T_c$ increases as one approaches the unitary limit, consistent with recent experiments, the paraconductivity exhibits non-monotonic behavior. Our results can be verified with the recently developed transport apparatus using mesoscopic channels.Comment: 8 pages and 4 figures including supplementary material

Evolution of Higgs mode in a Fermion Superfluid with Tunable Interactions

In this letter we present a coherent picture for the evolution of Higgs mode in both neutral and charged $s$-wave fermion superfluids, as the strength of attractive interaction between fermions increases from the BCS to the BEC regime. In the case of neutral fermionic superfluid, such as ultracold fermions, the Higgs mode is pushed to higher energy while at the same time, gradually loses its spectral weight as interaction strength increases toward the BEC regime, because the system is further tuned away from Lorentz invariance. On the other hand, when damping is taken into account, Higgs mode is significantly broadened due to coupling to phase mode in the whole BEC-BCS crossover. In the charged case of electron superconductor, the Anderson-Higgs mechanism gaps out the phase mode and suppresses the coupling between the Higgs and the phase modes, and consequently, stabilizes the Higgs mode.Comment: 5 figures, 9 pages, including supplementary materia

The semi-analytical method for damping of tubular transition layer damping structure

To solve the limited vibration consumption of the traditional tubular damping structure (TTDS), the tubular transition layer damping structure (TTLDS) is proposed; Based on viscoelastic materials and theories of thin cylindrical shells, the governing equation, the first order matrix differential equation describing vibration of TTLDS under harmonic excitation, is derived by considering the interaction between all layers and the dissipation caused by the shear deformation for transition layer and damping layer. By using the extended homogeneous capacity precision integration method to solve the control equation, a semi-analytical method for studying the vibration and damping characteristics of TTLDS is given. By way of comparison, the correctness of the method provided in paper is verified. At last, the influence of thickness, material and location of transition layer on damping effect is analyzed. The results show that the change for the thickness or material of the transition layer can make the structural damping effect change greatly, while the change for location of the transition layer plays only a few roles on the structural damping effect

A fast and memory efficient video transcoder for low bit rate wireless communications

Wireless video is one of the important applications sup-ported by upcoming 3G mobile communication systems. In this paper, we propose a fast and memory efficient DCT-domain video transcoder to convert a high quality MPEG2 video bit stream into a low bit rate MPEG4 stream with low spatial resolution for wireless video access. Compared to existing approaches, the proposed video transcoder can save more than 50 % of required memory. Furthermore, the com-putational complexity of the proposed method is less than 30 % of that required by existing methods. However, the video quality achieved by the proposed method and by ex-isting methods is hardly distinguishable for target bit rates of 384 kb/s and 256 kb/s, as shown in our experimental re-sults. 1

PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models

Generalizable 3D part segmentation is important but challenging in vision and robotics. Training deep models via conventional supervised methods requires large-scale 3D datasets with fine-grained part annotations, which are costly to collect. This paper explores an alternative way for low-shot part segmentation of 3D point clouds by leveraging a pretrained image-language model, GLIP, which achieves superior performance on open-vocabulary 2D detection. We transfer the rich knowledge from 2D to 3D through GLIP-based part detection on point cloud rendering and a novel 2D-to-3D label lifting algorithm. We also utilize multi-view 3D priors and few-shot prompt tuning to boost performance significantly. Extensive evaluation on PartNet and PartNet-Mobility datasets shows that our method enables excellent zero-shot 3D part segmentation. Our few-shot version not only outperforms existing few-shot approaches by a large margin but also achieves highly competitive results compared to the fully supervised counterpart. Furthermore, we demonstrate that our method can be directly applied to iPhone-scanned point clouds without significant domain gaps.Comment: CVPR 2023, project page: https://colin97.github.io/PartSLIP_page

Identification of Plk4 interacting partners and establishment of Plk4 stable cell lines.

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OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding

We introduce OpenShape, a method for learning multi-modal joint representations of text, image, and point clouds. We adopt the commonly used multi-modal contrastive learning framework for representation alignment, but with a specific focus on scaling up 3D representations to enable open-world 3D shape understanding. To achieve this, we scale up training data by ensembling multiple 3D datasets and propose several strategies to automatically filter and enrich noisy text descriptions. We also explore and compare strategies for scaling 3D backbone networks and introduce a novel hard negative mining module for more efficient training. We evaluate OpenShape on zero-shot 3D classification benchmarks and demonstrate its superior capabilities for open-world recognition. Specifically, OpenShape achieves a zero-shot accuracy of 46.8% on the 1,156-category Objaverse-LVIS benchmark, compared to less than 10% for existing methods. OpenShape also achieves an accuracy of 85.3% on ModelNet40, outperforming previous zero-shot baseline methods by 20% and performing on par with some fully-supervised methods. Furthermore, we show that our learned embeddings encode a wide range of visual and semantic concepts (e.g., subcategories, color, shape, style) and facilitate fine-grained text-3D and image-3D interactions. Due to their alignment with CLIP embeddings, our learned shape representations can also be integrated with off-the-shelf CLIP-based models for various applications, such as point cloud captioning and point cloud-conditioned image generation.Comment: Project Website: https://colin97.github.io/OpenShape

Roles of the spiA gene from Salmonella enteritidis in biofilm formation and virulence

Salmonella enteritidis has emerged as one of the most important food-borne pathogens for humans, and the formation of biofilms by this species may improve its resistance to disadvantageous conditions. The spiA gene of Salmonella typhimurium is essential for its virulence in host cells. However, the roles of the spiA gene in biofilm formation and virulence of S. enteritidis remain unclear. In this study we constructed a spiA gene mutant with a suicide plasmid. Phenotypic and biological analysis revealed that the mutant was similar to the wild-type strain in growth rate, morphology, and adherence to and invasion of epithelial cells. However, the mutant showed reduced biofilm formation in a quantitative microtitre assay and by scanning electron microscopy, and significantly decreased curli production and intracellular proliferation of macrophages during the biofilm phase. In addition, the spiA mutant was attenuated in a mouse model in both the exponential growth and biofilm phases. These data indicate that the spiA gene is involved in both biofilm formation and virulence of S. enteritidis